Abstract
Due to the growing number of diabetic patients, it is essential to organize and plan balanced nutrition. It is also necessary to recommend certain food products that need to be consumed by diabetic patients effectively. Therefore, this paper develops a novel concept to determine and classify diabetic patients thereby recommending a balanced food diet for a healthy lifestyle. This article comprises two significant phases namely the data pre-processing phase as well as the classification phase. In the classification phase, a novel adaptive identity preserving conditional generative adversarial network-based multi-objective whale slime mold optimization (AIPCGAN-MWSMO) algorithm is proposed to accurately determine the patients affected by diabetes and to recommend nutritional food products based on their health conditions. The proposed approach uses a composition of food integrated dataset (CoFID) dataset to determine the system performance. Finally, comparing the introduced approach over various existing state of art techniques is done with respect to various performance metrics and the accuracy rate achieved for the proposed AIPCGAN-MWSMO approach is 98%.
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Rachitha, M.V., Ramakrishna, M. MWSMO: Multi-objective Whale Slime Mold Optimization based Food Recommendation system for Diabetes patient using GAN model. Int. j. inf. tecnol. 15, 2357–2363 (2023). https://doi.org/10.1007/s41870-023-01228-4
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DOI: https://doi.org/10.1007/s41870-023-01228-4